Large scale detailed mapping of dengue vector breeding sites using street view images

摘要

Author summary Providing detailed environmental information on a large scale to effectively target dengue control efforts remains a challenge. In this paper we present the design and implementation of a pipeline to detect outdoor open containers which constitute potential dengue vector breeding sites from geotagged images and to create highly detailed container density maps at unprecedented scale. Specifically, we use convolutional neural networks to detect a variety of types of breeding site container types in Google street view images and use the container counts to create container density maps. Evaluation of the approach is carried out over three provinces in Thailand: Bangkok, Krabi, and Nakhon Si Thammarat. Our evaluation shows that the object recognition network can accurately recognize several of the most important types of containers in Thailand. The container counts obtained from the street view images agree well with container counts from available manual surveys. We further show that simple multi-linear models using container density values provide good predictions of Breteau index (number of positive containers per 100 houses inspected) values. This is the first study to present results validating container counts from image analysis against such data.